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Driss Chenouni
University Sidi Mohammed Ben Abdellah
Computer Science & IT Electrical & Electronics Engineering

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Combined cosine-linear regression model similarity with application to handwritten word spotting Youssef Elfakir; Ghizlane Khaissidi; Mostafa Mrabti; Driss Chenouni; Manal Boualam
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (642.954 KB) | DOI: 10.11591/ijece.v10i3.pp2367-2374

Abstract

The similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system.
Co-Authors Ghizlane Khaissidi Manal Boualam Mostafa Mrabti Youssef Elfakir